We follow the common episodic paradigm in [1]}, [2]}, [3]}, which is trained on the \(N\) -way \(K\) -shot setting. There are \(K\)  labeled samples and a number of unlabeled samples from \(N\)  categories composing of a few-shot task. The support set consists of \(N \times K\)  labeled samples, denoted as \(S= \lbrace x_i^s,y_i^s\rbrace _{i=1}^{N\times K}\) . And the query set is represented as \(Q= \lbrace x_i^q,y_i^q\rbrace _{i=1}^{M}\) , where \(M\)  is the total number of unlabeled samples. In vanilla FSL, the train set and test set belong to the same domain. While in CD-FSL, the train set and test set are sampled from different domains.
